This is very image codec specific: Normally a DCT-based (JPEG style) codec has mosquito/ringing noise especially on text. But within a neural texture/image codec that places a quantized DCT in the training loop (via ES), the neural net can learn how to suppress these artifacts quite effectively. This is a libjpeg Q=5 AC quantization matrix, applied XUASTC/XUBC7 style on the level 0 latent's spatial values - super low DCT quality.
The first image is the full texel resolution IDCT decoded level 0 latent, and the second is the fully decompressed image (the output from the ~500 weight neural network, after decoding the 2 latents and local "cell" UV as inputs).
The latent textures:
A latent can tolerate quantization artifacts that would be catastrophic in image space because joint training can rotate/warp the useful representation so those artifacts lie largely in low-sensitivity directions of a tiny learned decoder.